Our learning-based end-to-end multi-view stereo system, RIAV-MVS, aims to predict depth maps from a set of images, different views of the same scene with known camera poses, denoted by \(\mathcal {I} =\lbrace I_i\rbrace _{i=0}^{N-1}\) . More specifically, it uses one as the reference image and others as source images to infer the depth map of the reference one. Without loss of generality, we refer to the first image \(I_0\)  as reference view and others as source views \(\mathcal {I}^\mathcal {S}\) , with the superscript \((\cdot )^\mathcal {S}\)  for the set of source view indices \({1,2,...,N-1}\) . An overview of our approach is illustrated in Fig. REF . It consists of feature extraction (Sec. REF ), cost volume construction via plane-sweeping stereo [1]}, [2]} (Sec. REF ), and cost volume optimization and depth prediction (Sec. REF ). Details will be discussed as below.
